Accelerate your journey of protein discovery
Our technology enables optimization of proteins and RNAs for desired properties including binding affinity, stability, escape resistance, and low immunogenicity.
Design Multi-objective Fitness Function
Define desired properties and constraints
We work with your team to define a project-specific fitness function integrating evolutionary constraints, binding affinity, stability, expression, and manufacturability. Benchmarks are performed on public data to validate performance.
Design Multi-objective Fitness Function
Define desired properties and constraints
Literature → Constraints → Fitness Function
Few-shot Prediction & Experimental Testing
Select optimal variants to test
Our platform generates high-priority candidates using few-shot prediction models. Selected variants are tested experimentally — either in your lab or through CROs — to validate the fitness landscape and guide optimization.
Few-shot Prediction & Experimental Testing
Select optimal variants to test
Prediction → Selection → Wet-lab testing
Retrain & Optimize
Iterative learning with experimental feedback
We retrain the machine learning models on experimental data to refine predictions and generate new, optimized batches of candidates. This iterative DBTL cycle converges rapidly towards highly fit, robust protein designs.
Retrain & Optimize
Iterative learning with experimental feedback
Retrain → Next-generation design → DBTL Cycle